{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from matplotlib import pyplot as plt\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.preprocessing import PolynomialFeatures\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.metrics import mean_squared_error\n",
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_pl(degree):\n",
    "    return Pipeline([\n",
    "        ('poly',PolynomialFeatures(degree=degree)),\n",
    "        ('std_scaler',StandardScaler()),\n",
    "        ('lr',LinearRegression())\n",
    "    ])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.random.seed(666)\n",
    "x=np.random.uniform(-3.0,3.0,100)\n",
    "X=x.reshape(-1,1)\n",
    "y=x**2 * 0.5 + x  + 1 + np.random.normal(0,1,len(x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train,X_test,y_train,y_test=train_test_split(X,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_learning_curve(algo,X_train,X_test,y_train,y_test):\n",
    "    train_score = []\n",
    "    test_score = []\n",
    "    for i in range(1,len(X_train)):\n",
    "        algo.fit(X_train[:i],y_train[:i])\n",
    "        y_train_predict = algo.predict(X_train[:i])\n",
    "        y_test_predict = algo.predict(X_test)\n",
    "        train_score.append(mean_squared_error(y_train_predict,y_train[:i]))\n",
    "        test_score.append(mean_squared_error(y_test_predict,y_test))\n",
    "    plt.plot([i for i in range(1,len(X_train))],train_score,color='g',label='train')\n",
    "    plt.plot([i for i in range(1,len(X_train))],test_score,color='r',label='test')\n",
    "    plt.legend()\n",
    "    plt.axis([0,len(X_train),0,4])\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1137bc470>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_learning_curve(LinearRegression(),X_train,X_test,y_train,y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a1df51be0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_learning_curve(get_pl(degree=2),X_train,X_test,y_train,y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a1e0c0dd8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_learning_curve(get_pl(degree=20),X_train,X_test,y_train,y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
